Linear models capture well the relations between sensor data if the system is in a steady state. Establishing relationships on the same row of a data set means no time dependency. Even systems which are switched on and off during working periods can be...

Yesterday at ML Conference, which took place this year for the first time, I had a talk on cool bits of calculus and linear algebra that are useful and fun to know if you’re writing code for deep learning and/or machine learning. Originally, the...

I really wish I had the time to write an article about the conference, instead of just posting the slides! Predictive Analytics World was super inspiring, not just in a technical way but also as to the broader picture of today’s data science / AI...

This week in Kassel, [R]Kenntnistage 2017 took place, organised by EODA. It was all about Data Science (with R, mostly, as you could guess): Speakers presented interesting applications in industry, manufacturing, ecology, journalism and other fields,...

Yesterday, the Munich datageeks Data Day took place. It was a totally fun event – great to see how much is going on, data-science-wise, in and around Munich, and how many people are interested in the topic! (By the way, I think that more than half...

On Friday at DOAG Big Data Days, I presented one possible application of deep learning: using deep learning for automatic crack detection – with some background theory, a Keras model trained from scratch, and the use of VGG16 pretrained on Imagenet...

Last weekend, another edition of Trivadis Tech Event took place. As usual, it was great fun and a great source of inspiration. I had the occasion to talk about deep learning twice: One talk was an intro to DL4J (deeplearning4j), zooming in on a few aspects...

Earlier today, I presented at UseR! 2017 about HaskellR: a great piece of software, developed by Tweag I/O, that allows to seemlessly use R from Haskell. It was my first UseR!, it was a great experience, and if I had the time I’d like to write a...

More and more often, and in more and more different areas, deep learning is making its appearance in the world around us. Many small and medium businesses, however, will probably still think – Deep Learning, that’s for Google, Facebook &...

Yesterday at Trivadis Tech Event, I talked about R for Hackers. It was the first session slot on Sunday morning, it was a crazy, nerdy topic, and yet there were, like, 30 people attending! An emphatic thank you to everyone who came! R a crazy, nerdy topic...

Yesterday at PASS Meetup Munich, I talked about R for SQListas – thanks again for your interest and attention guys, it was a very nice evening! Actually, in addition to the content from that original presentation, which I’ve also covered in...

R for SQListas, part 2 Welcome to part 2 of my “R for SQListas” series. Last time, it was all about how to get started with R if you’re a SQL girl (or guy)- and that basically meant an introduction to Hadley Wickham’s dplyr and...

R for SQListas, what’s that about? This is the 2-part blog version of a talk I’ve given at DOAG Conference this week. I’ve also uploaded the slides (no ppt; just pretty R presentation ) to the articles section, but if you’d like...

The recent interview in O’Reilly’s Data Show Podcast covers in-memory streaming technologies like Apache Spark, Alluxio, and other open source technologies. More interestingly, deep learning is recommended for time series analysis. Particularly...

F1 is a diagnostic tool with fine-tuned balance between ying and yang of precision and recall. The new episode of Data Sceptic Podcast illustrates its utility in a plausible story. They tell us about the vivid analogy to design choices and the typical...